Audio-to-Audio
Moshi
English
speech-to-speech
full-duplex
spoken-dialogue
conversational-ai
voice-agent
voice-assistant
real-time
indian-english
indian-accent
india
customer-support
call-center
barge-in
mimi
lora
audio
Instructions to use IOTEverythin/roxi-duplex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Moshi
How to use IOTEverythin/roxi-duplex with Moshi:
# pip install moshi # Run the interactive web server python -m moshi.server --hf-repo "IOTEverythin/roxi-duplex" # Then open https://localhost:8998 in your browser
# pip install moshi import torch from moshi.models import loaders # Load checkpoint info from HuggingFace checkpoint = loaders.CheckpointInfo.from_hf_repo("IOTEverythin/roxi-duplex") # Load the Mimi audio codec mimi = checkpoint.get_mimi(device="cuda") mimi.set_num_codebooks(8) # Encode audio (24kHz, mono) wav = torch.randn(1, 1, 24000 * 10) # [batch, channels, samples] with torch.no_grad(): codes = mimi.encode(wav.cuda()) decoded = mimi.decode(codes) - Notebooks
- Google Colab
- Kaggle
| data: {train_data: "/home/coherent_pro_full", eval_data: "", shuffle: true} | |
| moshi_paths: {hf_repo_id: "kyutai/moshiko-pytorch-bf16"} | |
| full_finetuning: false | |
| lora: {enable: true, rank: 64, scaling: 2., ft_embed: false} | |
| first_codebook_weight_multiplier: 100. | |
| text_padding_weight: .5 | |
| duration_sec: 20 | |
| batch_size: 1 | |
| max_steps: 1500 | |
| gradient_checkpointing: true | |
| optim: {lr: 2.e-5, weight_decay: 0.1, pct_start: 0.05} | |
| seed: 0 | |
| log_freq: 50 | |
| eval_freq: 20000 | |
| do_eval: false | |
| do_ckpt: true | |
| ckpt_freq: 1500 | |
| save_adapters: true | |
| run_dir: "/home/moshi_run_pro_full" | |